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Sensors 2013, 13(3), 3270-3298; doi:10.3390/s130303270
Article

Robust Lane Sensing and Departure Warning under Shadows and Occlusions

 and *
Received: 19 February 2013; in revised form: 2 March 2013 / Accepted: 4 March 2013 / Published: 11 March 2013
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Abstract: A prerequisite for any system that enhances drivers’ awareness of road conditions and threatening situations is the correct sensing of the road geometry and the vehicle’s relative pose with respect to the lane despite shadows and occlusions. In this paper we propose an approach for lane segmentation and tracking that is robust to varying shadows and occlusions. The approach involves color-based clustering, the use of MSAC for outlier removal and curvature estimation, and also the tracking of lane boundaries. Lane boundaries are modeled as planar curves residing in 3D-space using an inverse perspective mapping, instead of the traditional tracking of lanes in the image space, i.e., the segmented lane boundary points are 3D points in a coordinate frame fixed to the vehicle that have a depth component and belong to a plane tangent to the vehicle’s wheels, rather than 2D points in the image space without depth information. The measurement noise and disturbances due to vehicle vibrations are reduced using an extended Kalman filter that involves a 6-DOF motion model for the vehicle, as well as measurements about the road’s banking and slope angles. Additional contributions of the paper include: (i) the comparison of textural features obtained from a bank of Gabor filters and from a GMRF model; and (ii) the experimental validation of the quadratic and cubic approximations to the clothoid model for the lane boundaries. The results show that the proposed approach performs better than the traditional gradient-based approach under different levels of difficulty caused by shadows and occlusions.
Keywords: road sensing; lane detection and tracking; lane departure warning; mean-shift clustering; gabor filters; Gaussian Markov Random Fields; RANSAC road sensing; lane detection and tracking; lane departure warning; mean-shift clustering; gabor filters; Gaussian Markov Random Fields; RANSAC
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Tapia-Espinoza, R.; Torres-Torriti, M. Robust Lane Sensing and Departure Warning under Shadows and Occlusions. Sensors 2013, 13, 3270-3298.

AMA Style

Tapia-Espinoza R, Torres-Torriti M. Robust Lane Sensing and Departure Warning under Shadows and Occlusions. Sensors. 2013; 13(3):3270-3298.

Chicago/Turabian Style

Tapia-Espinoza, Rodolfo; Torres-Torriti, Miguel. 2013. "Robust Lane Sensing and Departure Warning under Shadows and Occlusions." Sensors 13, no. 3: 3270-3298.



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